A Research Study On Emotion Recognition Systems For Supporting Mental Health Interventions And Promoting Sustainability In Public Environments Using Latest Computer Applications

Abstract

Mental health concerns are increasingly prevalent in modern societies, necessitating the integration of advanced technological interventions to provide timely, scalable, and sustainable solutions. This research study investigates the development and implementation of emotion recognition systems (ERS) to support mental health interventions and promote sustainability in public environments. Leveraging the latest computer applications, including machine learning (ML), deep learning (DL), and artificial intelligence (AI) algorithms, the proposed framework aims to detect and analyze emotional states in real-time using multimodal data sources such as facial expressions, voice patterns, and physiological signals. The study explores the integration of ERS within public infrastructures like educational institutions, workplaces, and healthcare facilities to provide personalized, non-intrusive, and continuous mental health support. Furthermore, the research emphasizes energy-efficient computing models, privacy-preserving data handling techniques, and sustainable hardware deployment strategies to ensure long-term environmental and social benefits. Experimental evaluations and case studies demonstrate the potential of this approach to enhance emotional well-being, reduce mental health disparities, and create technologically empowered sustainable public spaces.